Deep Feature Fusion for Enhanced Medical Image Retrieval Using CNN and Texture Descriptors
DOI:
https://doi.org/10.26438/ijcse/v13i11.4552Keywords:
Medical Image Retrieval, Deep Learning, Convolutional Neural Networks (CNN), Feature Fusion, Local Binary Pattern (LBP), Gabor Filter, Texture Descriptors, Content-Based Image Retrieval (CBIR), Similarity MetricsAbstract
Medical image retrieval is an important tool for supporting doctors in identifying diseases. Earlier systems mainly used handcrafted features like color and texture, but such features often fail to capture the complex patterns in medical images. In this paper, we present a hybrid method that combines deep features from pretrained CNN models with traditional texture-based features like Local Binary Pattern (LBP) and Gabor filters. By merging deep learning with texture descriptors, our approach enhances the quality of image retrieval. Experiments are performed on popular medical dataset like BreakHis and various distance metrics such as Euclidean and Cosine are used for similarity comparison. The results show that our fusion-based system performs better than standard techniques in terms of precision and retrieval accuracy. This confirms the usefulness of combining deep features with handcrafted features for improving medical image search systems.
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